Semantic rules provide definitions of relations between words and symbols. There exists Semantic Web Rule Language (SWRL) specification for enabling creation of Ontology Web Language (OWL)/Resource Description Framework (RDF) titles. Further, Jena rules are implemented which define a semantic web rule specification. Jena rule syntax is of a predefined format which is given as an antecedent followed by a consequent where both the antecedent and the consequent are conjunctions of atoms written as aIΛ . . . Λan. Further, in Jena rule syntax, variables are indicated using standard convention of prefixing them with a question mark (for example ?x), where x is the variable. Other elements found in the Jena rules are URIs of ontology classes, properties, individuals and literals, along with any built-in functions.
In order to write a rule in the Jena rule syntax, a user needs to have knowledge of Jena rule syntax as well as the RDF/OWL ontology schema and individuals information such as classes URIs, properties URIs, individuals URIs and so on. When such rules need to be created, updated and read by a user, the user need to be an expert and may require knowledge in field of Jena rule syntax and RDF. For a business user, there may be a need to hire a specialist for performing the reading and writing of such rules. However, for the specialist too, the task of writing and reading may be cumbersome, time taking and error prone process due to the sheer number of URIs being available in the ontology/RDF data or may tend to commit typographical errors.
Existing systems disclose one or more conventional techniques for automated generation of rules in order to eliminate the need for the specialists. One of the techniques aims in simplifying querying of ontologies. One or more queries for querying ontology are automatically generated in a language suitable for querying the ontology. A user may enter a query in a simple language such as a natural language query. In response, the one or more queries capable of querying the ontology are automatically generated in a second language. The one or more automatically generated queries may then be used to query one or more ontologies. However, there is a need for the user to interpret the query accurately in the natural language for obtaining the requiring queries.
Another conventional technique discloses an aspect of combining ontology representations and agents optimization for performance which may capitalize strengths of a user approaches and reduce the user weaknesses. Automatic translators are implemented to convert ontological representations, hand-coded procedures for ontological inference, and explanation-based learning to cache inference. In another conventional technique, a class diagram representing ontology is transformed to a standard representation of ontology based on Rational Software Architect (RSA) and its model transformation capabilities. The RSA transformation framework uses a Visitor-like pattern for iterating over all of Unified Modeling Language (UML) elements in a UML diagram. The framework allows the developer to configure ‘Rule’ classes that are executed when different types of UML elements are encountered. However, the knowledge regarding class diagram representation is required for a user to obtain the ontology and the conventional techniques do not disclose a user friendly interface for generating a rule. Further, conventional method may not be reliable for generating a semantic rule when the user is a non-programmer.